Rescue Missions Research Articles

Design and application of UAV single target tracking system based on YOLOv5

In aerial reconnaissance, pursuit, and post-disaster search and rescue, rotorcraft UAVs are valued for their agility. A key challenge is target tracking in complex environments with no prior information, dense obstacles, GNSS signal loss, and occluded targets. This paper introduces a YOLOv5-based UAV tracking system to address detection and path planning. The quadrotor UAV model ensures stable flight through mathematical modeling and simulation. YOLOv5 enhances detection performance with real-time tracking. Feedback control enables autonomous obstacle avoidance and target tracking. Results show adaptability and robustness, supporting reconnaissance and rescue missions effectively.

Transformer-Based Reinforcement Learning for Multi-Robot Autonomous Exploration.

A map of the environment is the basis for the robot's navigation. Multi-robot collaborative autonomous exploration allows for rapidly constructing maps of unknown environments, essential for application areas such as search and rescue missions. Traditional autonomous exploration methods are inefficient due to the repetitive exploration problem. For this reason, we propose a multi-robot autonomous exploration method based on the Transformer model. Our multi-agent deep reinforcement learning method includes a multi-agent learning method to effectively improve exploration efficiency. We conducted experiments comparing our proposed method with existing methods in a simulation environment, and the experimental results showed that our proposed method had a good performance and a specific generalization ability.

Optimizing drone-assisted victim localization and identification in mass-disaster management: a study on feasible flying patterns and technical specifications

The prompt emphasizes the importance of identifying victims in a disaster area within 48 hours and highlights the potential benefits of using drones in search and rescue missions. However, the use of drones is limited by factors such as battery life, processing speed, and communication range. To address these limitations, the paper presents a detailed research study on the most effective flying pattern for drones during search and rescue missions. The study utilized energy consumption and coverage area as performance metrics and collected precise images that could be analyzed by the forensic team. The research was conducted using OMNET++ and fieldwork at Pulau Sebang, Melaka, in collaboration with search and rescue agencies in Malaysia. The results suggest that the square flying pattern is the most effective, as it provides the highest coverage area with reasonable energy utilization. Both simulation and fieldwork results showed coverage of 100% and 97.96%, respectively, for this pattern. Additionally, the paper provides technical specifications for rescue teams to use when deploying drones during search and rescue missions.

Dynamic modeling and simulation of a torque-controlled spatial quadruped robot

AbstractEvolution has shown that legged locomotion is most adequate for tasks requiring versatile movement on land, allowing animals to traverse a wide variety of environments ranging from natural terrain to artificial, man-made landscapes with great ease. By employing well-designed control schemes, this ability could be replicated for legged robots, enabling them to be used in critical situations that still pose great danger to human integrity, such as search and rescue missions, inspection of hazardous areas, and even space exploration. This work characterizes the quadruped robot and contact dynamics that will compose our in-house simulator to be used for prototyping locomotion control schemes applied to quadruped robots. The proposed simulator computes the robot dynamics using the Recursive Newton-Euler and Composite-Rigid-Body algorithms with a few modifications to make certain aspects relevant for contact detection and control more easily accessible; furthermore, a compliant contact force method alongside stick-slip friction modeled the contact dynamics. To allow the robot to move, a simple PD-independent joint controller was implemented to track a desired leg trajectory. With the same robot and controller implemented using the MuJoCo simulation software, this work evaluates the proposed simulator by comparing characteristic locomotion signals such as the trunk pose and the ground reaction forces. Results showed similar behavior for both simulators, especially with regard to the contact detection, despite the significantly different contact models. Lastly, final remarks to enhance our simulator’s performance are suggested to be explored in future works.

A clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network

The current paper presents a clustering method for energy efficient management of heterogeneous nodes of a flying ad hoc network (FANET). The technological advances of the last decade gave rise to emerging technologies. Unmanned aerial vehicles (UAVs) are small aircraft that proved their usefulness for different tasks nowadays. They can collaborate to achieve missions especially in areas where traditional networks cannot work or cannot accede. A FANET is composed by a number of these aircraft. For alike networks, the resources are limited. Indeed, an efficient energy management is required to extend the life of the network. This work is a clustering method for heterogeneous nodes of a FANET, each node is equipped with one sensor, and four different sensors are used. Clustering is grouping nodes with the aim of efficiency improvement. The clustering is done before the beginning of the rescue mission and depends on the types of sensors the nodes are equipped with and. The master election depends on the available energy of each one of the nodes. The simulation is done with a discrete event simulator (DES) and the results are compared to the algorithm of glowworm swarm optimization (GSO) to demonstrate the effectiveness of the suggested technique.

Optimization of Predefined-Time Agent-Scheduling Strategy Based on PPO

In this paper, we introduce an agent rescue scheduling approach grounded in proximal policy optimization, coupled with a singularity-free predefined-time control strategy. The primary objective of this methodology is to bolster the efficiency and precision of rescue missions. Firstly, we have designed an evaluation function closely related to the average flying distance of agents, which provides a quantitative benchmark for assessing different scheduling schemes and assists in optimizing the allocation of rescue resources. Secondly, we have developed a scheduling strategy optimization method using the Proximal Policy Optimization (PPO) algorithm. This method can automatically learn and adjust scheduling strategies to adapt to complex rescue environments and varying task demands. The evaluation function provides crucial feedback signals for the PPO algorithm, ensuring that the algorithm can precisely adjust the scheduling strategies to achieve optimal results. Thirdly, aiming to attain stability and precision in agent navigation to designated positions, we formulate a singularity-free predefined-time fuzzy adaptive tracking control strategy. This approach dynamically modulates control parameters in reaction to external disturbances and uncertainties, thus ensuring the precise arrival of agents at their destinations within the predefined time. Finally, to substantiate the validity of our proposed approach, we crafted a simulation environment in Python 3.7, engaging in a comparative analysis between the PPO and the other optimization method, Deep Q-network (DQN), utilizing the variation in reward values as the benchmark for evaluation.

The Future of Unmanned Aerial Vehicles (UAVs) Has No Batteries

Unmanned Aerial Vehicles (UAVs) hold immense potential across various fields, including precision agriculture, rescue missions, delivery services, weather monitoring, and many more. Despite this promise, the limited flight duration of current UAVs stands as a significant obstacle to their widespread adoption. Attempting to extend flight time by solar panel charging during midflight is not viable due to battery limitations and the eventual need for replacement. This article highlights exciting efforts towards the future of a UAV that survives in the air by harvesting solar energy and performing long-term missions without battery re-charging and/or replacement.

“It Had to Be Us”: The Rudolph Effect and the Inclusion of Unconventional Leaders

More insights are needed about how to overcome situations where conventional leadership breaks down. By teasing apart concepts and processes associated with the inclusion of unconventional leaders, Greimas and Courtès’ actantial model is leveraged to collate the relationships threaded by the actors involved with the 2018 rescue mission of the Wild Boars soccer team in Thailand. What we label as the Rudolph Effect describes how outcasts, misfits, and mavericks can be redefined as valuable members of a leadership team. This case study suggests that providing opportunities for unconventional team members to demonstrate their skills and capital allows them to shift from supporting or advisory roles to more decisional and leadership roles. Both theoretical and practical managerial tools that facilitate the development and inclusion of unconventional leaders are provided.

Joint optimization of communication and mission performance for multi-UAV collaboration network: A multi-agent reinforcement learning method

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.

A 6-minute protocol, combining mental imagery practices and breathing exercises, promotes hand-grip strength in firefighters: a series of "N-of-1" trials.

To carry out their victim rescue and fire-fighting missions, firefighters perform high levels of physical exertion and among them strenuous muscular activity. A specific mental preparation protocol that could induce better strength performance throughout their 24-hour schedule is a major issue for firefighters. This case report aims to examine whether a 6-minute Intervention combining mental imagery practices and breathing exercises, specifically designed to be used while travelling between the fire station and the mission site, would be able to promote maximum strength performance. A series of three Single Case Experimental Designs (SCED) was conducted in single blind design to investigate the effects of repeated challenge-withdrawals between the Intervention and the Baseline on the maximum voluntary isometric contraction (MVIC) strength of the hand-grip. Data analyses revealed that 62.5% to 100% of the hand-grip strength values during the Intervention periods were greater than or equal to the maximum data point recorded in the Baseline periods. The effect sizes of these highlighted increases of the hand-grip strength performance revealed by the percentage of non-overlapping data (PND) were 75% i.e., moderately effective on average. Such a "psyching-up" practice before an upcoming muscular activity can promote muscular strength in firefighters. These results have led French firefighter departments to integrate the teaching of these practices into the initial instruction of firefighters, and remains to be confirmed by a randomised control trial.

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Programmable Microfluidic‐Assisted Highly Conductive Hydrogel Patches for Customizable Soft Electronics

AbstractThe utilization of hydrogels in soft electronics has led to significant progress in the field of wearable and implantable devices. However, challenges persist in hydrogel electronics, including the delicate equilibrium between stretchability and electrical conductivity, intricacies in miniaturization, and susceptibility to dehydration. Here, a lignin‐polyacrylamide (Ag‐LPA) hydrogel composite endowed with anti‐freeze, self‐adhesive, exceptional water retention properties, and high stretchability (1072%) is presented. Notably, this composite demonstrated impressive electrical conductivity at room temperature (47.924 S cm−1) and extremely cold temperatures (42.507 S cm−1). It is further proposed for microfluidic‐assisted hydrogel patches (MAHPs) to facilitate customizable designs of the Ag‐LPA hydrogel composite. This approach enhances water retention and offers versatility in packaging materials, making it a promising choice for enduring soft electronics applications. As a proof‐of‐concept, soft electronics across diverse applications and dimensions, encompassing healthcare monitoring, environmental temperature sensing, and 3D‐spring pressure monitoring electronics are successfully developed. The scenery of an extremely cold environment is further extended. The conductivity of the embedded Ag‐LPA hydrogel composite unveils the potential of MAHPs in polar rescue missions. It is envisioned that MAHPs will impact the development of sophisticated and tailored soft electronics, thereby forging new frontiers in engineering applications.

Distance-Enhanced Hybrid Hierarchical Modulation and QAM Modulation Schemes for UAV Terahertz Communications

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations require substantial data and video transmission, demanding significant spectral resources. The ultra-broad bandwidth of 0.1–10 THz in the Terahertz (THz) frequency range is essential for future UAV-based wireless communications. However, the available bandwidth in the THz frequency spectrum varies with transmission distance. To enhance spectral efficiency over this variable bandwidth, we propose using hierarchical modulation (HM) in the overlapped spectrum and traditional quadrature amplitude modulation (QAM) in the non-overlapped spectrum for closer users. Furthermore, we analyze the single-user case and utilize the block-coordinated descent (BCD) method to jointly optimize the modulation order, subcarrier bandwidth, and sub-band power to improve the system sum rate. Finally, considering the mobility and randomness of UAV users, we design a modulation switching rule to dynamically adjust to changes in distance as users move, thereby enhancing data rates. Simulation results demonstrate superior performance in data rate and design complexity compared to existing methods such as hierarchical bandwidth modulation (HBM) and HM schemes.

Robot remote control using virtual reality headset: studying sense of agency with subjective distance estimates

Mobile robots have many applications in the modern world. The autonomy of robots is increasing, but critical cases like search and rescue missions must involve the possibility of human intervention for ethical reasons and safety. To achieve effective human–robot interaction, the operator needs to have a sense of agency (SoA) over the activities of the robot. One possible way to increase one's SoA in remote control could be the use of VR technology. The remote control situation has some important features, so indicators of SoA need to be reproduced there independently. In our study, participants controlled a mobile robot using either a monitor or a VR-headset as an output device. In both cases, active control was contrasted with passive observation of the robot's movement. In each trial, participants estimated the distance traveled by the robot—a putative implicit indicator of SoA. A significant difference between subjective distance estimates was found in the active and passive conditions with the monitor, but not in the active and passive conditions with VR. The effect obtained in the monitor conditions suggests that distance estimates can be used as an implicit indicator of SoA in robot remote control. We believe that the lack of difference between the active and passive conditions in VR was caused by motion sickness due to a mismatch of visual and vestibular sensory cues, leading to a weakened SoA.

Minimum time search using ant colony optimization for multiple fixed-wing UAVs in dynamic environments

The use of multiple fixed-wing unmanned aerial vehicles in search and rescue missions after natural disasters has become of great interest as they can search large areas and find survivors as quickly as possible. This paper discusses a minimum time cooperative search scheme that utilizes ant colony optimization and new heuristic functions to tackle various constraints in a dynamic environment. The study makes novel use of Dubins curves in the heuristic functions to consider the kinematic limitations of fixed-wing UAVs when planning tangent continuity paths. Furthermore, a novel probabilistic approach is introduced to model the uncertainties induced by dynamic obstacles and determine optimal search paths that are safe and practical in a grid search environment. The performance of the proposed search algorithm is tested through two-dimensional and three-dimensional simulations, statistical analysis, and comparison with other well-known optimization algorithms. To randomize the simulated cooperative search, different search scenarios with static and dynamic obstacles are run several times.

Energy-Aware Hierarchical Reinforcement Learning Based on the Predictive Energy Consumption Algorithm for Search and Rescue Aerial Robots in Unknown Environments

Aerial robots (drones) offer critical advantages in missions where human participation is impeded due to hazardous conditions. Among these, search and rescue missions in disaster-stricken areas are particularly challenging due to the dynamic and unpredictable nature of the environment, often compounded by the lack of reliable environmental models and limited ground system communication. In such scenarios, autonomous aerial robots’ operation becomes essential. This paper introduces a novel hierarchical reinforcement learning-based algorithm to address the critical limitation of the aerial robot’s battery life. Central to our approach is the integration of a long short-term memory (LSTM) model, designed for precise battery consumption prediction. This model is incorporated into our HRL framework, empowering a high-level controller to set feasible and energy-efficient goals for a low-level controller. By optimizing battery usage, our algorithm enhances the aerial robot’s ability to deliver rescue packs to multiple survivors without the frequent need for recharging. Furthermore, we augment our HRL approach with hindsight experience replay at the low level to improve its sample efficiency.

Protuberance placement mastery: Shock wave control integration with Coanda effect to thrust vectoring on a sonic jet

Thrust Vector Control (TVC) is a technique that allows precise control over the direction and velocity of an aerial vehicle. This paper presents a novel approach combining shock wave control and the Coanda effect to apply this technique in sonic flows. The objective is to achieve more eligible and operational control over the deviation angles of the thrust vector. The research is conducted through numerical simulations and experimental tests to investigate the impact of protuberance location (1, 3.5, and 5 mm), protuberance width (1.5, 2 mm), depth of penetration (2, 6, and 10%), and nozzle pressure ratio (ranging from 2.1 to 4) on the flow field, thrust vector angle, and thrust value of the nozzle. The system's behavior depends on the protuberance location, pressure ratio, and penetration depth. The location of the protuberance influences the shocks and flow separation from the Coanda flap. Increasing the penetration depth up to 10% enhances system stability and reduces the protuberance location's influence on flow deviation. The study highlights the significant effect of protuberance placement on thrust loss, with higher penetration depths resulting in a more significant decrease. The system's behavior in terms of flow deviation is more stable at specific penetration depths and pressure ratios. The results show that the proposed technique achieves a maximum deviation angle of 83° for a sonic jet. In addition, the findings contribute to understanding TVC using protuberances and the Coanda effect. The proposed technique offers advantages in simplicity, reliability, and control performance. It opens possibilities for efficient and maneuverable aerospace vehicles, with applications in UAVs, surveillance, and search and rescue missions.

UAV path planning algorithm based on Deep Q-Learning to search for a floating lost target in the ocean

In the context of real world application, Search and Rescue Missions on the ocean surface remain a complex task due to the large-scale area and the forces of the ocean currents, spreading lost targets and debris in an unpredictable way. In this work, we present a Path Planning Approach to search for a lost target on ocean surface using a swarm of UAVs. The combination of GlobCurrent dataset and a Lagrangian simulator is used to determine where the particles are moved by the ocean currents forces while Deep Q-learning algorithm is applied to learn from their dynamics. The evaluation results of the trained models show that our search strategy is effective and efficient. Over a total search area (red Sea zone), surface of 453422 Km2, we have shown that our strategy Search Success Rate is 98.61%, the maximum Search Time to detection is 15 days and the average Search Time to detection is almost 15 h.

Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar.

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included.